Amazon Web Services Updated SAP-C02 Exam Questions and Answers by alya

Create an S3 Bucket:

Log in to the AWS Management Console and navigate to Amazon S3.

Create a new S3 bucket that will serve as the destination for the application data.

Deploy AWS Storage Gateway:

Download and deploy the AWS Storage Gateway virtual machine (VM) on your on-premises environment. This VM can be deployed on VMware ESXi, Microsoft Hyper-V, or Linux KVM.

Configure the File Gateway:

Configure the deployed Storage Gateway as a file gateway. This will enable it to present Amazon S3 buckets as SMB file shares to your on-premises applications.

Create a New File Share:

Within the Storage Gateway configuration, create a new file share that is associated with the S3 bucket you created earlier. This file share will use the SMB protocol, allowing your on-premises applications to access the S3 bucket as if it were a local SMB file share.

Copy Data to the File Gateway:

Use your preferred method (such as robocopy, rsync, or similar tools) to copy data from the on-premises storage to the newly created file gateway endpoint. This data will be stored in the S3 bucket, maintaining accessibility through SMB.

Ensure Secure and Efficient Data Transfer:

AWS Storage Gateway ensures that all data in transit is encrypted using TLS, providing secure data transfer to AWS. It also provides local caching for frequently accessed data, improving access performance for on-premises applications.

This approach allows your existing on-premises applications to continue accessing data via SMB while leveraging the scalability and durability of Amazon S3.

References

AWS Storage Gateway Overview【67】.

AWS DataSync and Storage Gateway Hybrid Architecture【66】.

AWS S3 File Gateway Details【68】.

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The company wants to move from a manual EC2 and EBS-based workflow to a containerized application on Amazon EKS and automate data movement. The solution must:

Support automated transfer of raw and processed data.

Offer multiprotocol support.

Be directly usable from the EKS cluster as a mounted volume.

Minimize operational effort by using managed services where possible.

AWS DataSync is a managed service designed to move data between on-premises storage and AWS storage services or between AWS storage services. It can perform scheduled or continuous transfers with minimal operational overhead. For storage accessible from Amazon EKS, a shared file system that supports mounting as a volume is appropriate.

Amazon FSx for NetApp ONTAP provides a fully managed file system with multiprotocol support, including NFS and SMB, and supports features such as snapshots and storage efficiencies. Because it supports multiple protocols, it satisfies the requirement for multiprotocol access and can be mounted by applications running in Amazon EKS using standard Kubernetes persistent volume mechanisms.

In the correct solution (option C), DataSync is used to copy raw data from the on-premises environment to FSx for NetApp ONTAP. The FSx for NetApp ONTAP file system is then mounted as a volume in the EKS cluster, allowing the containerized analytics processing logic to read and write data directly. After processing, DataSync is again used to copy processed data from FSx for NetApp ONTAP to Amazon S3 for long-term storage. This leverages DataSync’s native integration with both FSx for NetApp ONTAP and Amazon S3, and avoids the need to run or manage custom upload tooling.

Option A uses Amazon EFS, which supports NFS but does not provide multiprotocol support (for example, SMB), so it does not fully meet the multiprotocol requirement. It also introduces AWS Transfer for SFTP for the processed data upload, which adds an additional managed endpoint and SFTP-based flow, increasing complexity relative to using DataSync end-to-end.

Option B uses Amazon FSx for Lustre, which is optimized for high-performance compute workloads and integrates well with S3, but it is not a multiprotocol file system and is typically accessed via NFS. It does not meet the stated multiprotocol requirement.

Option D uses FSx for NetApp ONTAP (which supports multiprotocol) but relies on AWS Transfer for SFTP to move processed data to S3. While this can work, it adds another managed input endpoint and requires SFTP client configuration and management. Using DataSync directly from FSx for NetApp ONTAP to Amazon S3 (as in option C) is more straightforward, better suited for automated large-scale transfers, and involves less operational overhead.

Therefore, option C meets all the requirements with the least operational effort by using DataSync with FSx for NetApp ONTAP and S3.

The core issue is high latency when serving images during traffic spikes. The current design resizes images dynamically and caches them locally inside ECS containers. With AWS Fargate, containers are ephemeral and scale in and out based on load. Local container caches are not shared across tasks, so cache hit rates drop during scaling events, forcing repeated image resizing. This increases CPU usage, startup latency, and response times under load.

The lowest operational overhead and most effective solution is to offload image delivery entirely from the application layer and use edge caching. Amazon CloudFront is designed to cache static content at edge locations close to end users, dramatically reducing latency and backend load. Amazon S3 is a durable, highly scalable object store that is well suited for serving static assets such as images.

Option A moves image delivery to a CloudFront + S3 architecture. By pre-scaling images and storing them in S3, the application no longer performs on-demand image resizing during requests. CloudFront caches the images at edge locations, so subsequent requests are served directly from the edge with minimal latency. The ALB remains the origin for dynamic content, while image requests are routed to the S3 origin using cache behaviors. This approach is fully managed, highly scalable, and requires minimal changes to the application logic once images are pre-generated.

Option B introduces ElastiCache, which adds operational overhead and complexity. The application must manage cache keys, eviction, and failure scenarios. ElastiCache is better suited for low-latency data access patterns rather than large static objects like images, and it does not provide global edge caching.

Option C adds unnecessary complexity by introducing an Aurora database as part of the caching layer. Databases are not designed to manage cache metadata for static assets at scale, and this design increases operational overhead without improving latency compared to CloudFront.

Option D is not appropriate because API Gateway caching is intended for API responses, not for efficient delivery of large static binary objects such as images. Replacing the ALB with API Gateway also introduces request size, payload, and cost considerations and does not provide global edge caching comparable to CloudFront for image delivery.

Therefore, using Amazon CloudFront with Amazon S3 for pre-scaled images provides the lowest latency, highest scalability, and least operational overhead.